The weights and biases of each neuron in the hidden layer
 define the position and width of a radial basis function.
 Each linear output neuron forms a weighted sum of these
 radial basis functions.  With the correct weight and bias 
 values for each layer, and enough hidden neurons, a radial
 basis network can fit any function with any desired
 accuracy.

 >> plot(p,radbas(p)+radbas(p-1.5)+.05 radbas(p+2),'m-');

 Above is an example of three radial basis functions (in
 blue) that are scaled and summed to produce a new function
 (magenta).